Capstone Project - The Battle of Neighborhoods

Applied Data Science Capstone by IBM/Coursera
Author: Mariusz Machczyński

Table of contents

Introduction / Business Problem

The world, where we are living, seems to be very big and many places are known to us by names or photos only. Of course, during vacations we have posibility to travel across and admire them for a while. Unfortunately, there is nothing for free, so huge part of our lifes we spend at work.

Nowadays multinational companies are very common, and their current and new employees have possibility to choose preferrable locations among of available ones. Of course, there are a lot of the luckiest employees, who may work remotely from their homes, but number of jobs which can be done in that way is rather limited. In many cases we must relocate to another place, very often to another city or even another country. Relocation to another place is stressful and may influence on employees' efficiency at work, so it is not the issue for employees themselves only, but for employers too. When a company have a few sites where an employee can work, then by providing possibility of choice to the employee, the company may reduce relocation stress and be more satisfied with the employee's work results. Of course, such situation takes place more often for new employees than for old ones, but old ones very often can do it themselves using internal jobs market, which is very common in that kind of companies.

Usually we feel comfortable when we are living in the environment known to us. When we must change it due to relocation then our stress will be smaller when new one is similar to the old one. But how to pick up the right one among few proposed? That problem can be solved by data science, which can show us similarities and dissimilarities between different places, and allows us to make better choice.

Let us assume, that there is a company, hereinafter named as Company, which has its production sites in three different cities: New York, Chicago and Dallas. Company is recruiting a new employee for a job, which can be done at any of its sites, and would like to provide to the employee the most convenient living environment, because the employee's skills are absolutely needed for Company. The employee is living in city Toronto and relocation to new place is required.

Due to the fact, that it is crucial to Company to employ the best of the best, Company decides to increase attractiveness of its job offer not only by giving free choice of three different places to live but also by providing them ordered by similarity to the place, where the employee currently lives. So, it is natural, that Company asked a data scientist to create the relevant comparison, which may convince the hesitating employee.

Data

Data needed to compare those four different cities will be a combination of:

  • list of neighborhoods of each city, data sources: various publicly available ones
  • geolocation data of each neighbourhood mentioned above, data source: Google Geocoding
  • venues exploration of each neighborhood mentioned above, data source: Foursquare

The comparison will be done at three different levels:

  • neighborhoods level: it provides sets of similar neighborhoods for different cities
  • boroughs level: it provides sets of similar boroughs for different cities
  • cities level: it provides ordered list of cities starting from the most similar one to Toronto

Collected data will be processed to find out similarities at all levels by segmentation and clustering neighborhoods of all cities. k-means clustering algorithm will be used for clustering neighborhoods. To avoid potential data difference between venues explored in all neighborhoods of a city, all boroughs of the city and the city itself, venues will be retrieved for neighborhoods only and they will be concatenated for boroughs and cities levels.

Cites and boroughs similarity will be obtained by distance calculation between their features, which are cluster labels obtained for neighborhoods, then ordered into the list.

The result of the comparison at cities level will give the employee easy choice of the most similar city to that, where the employee currently lives. The result of comparison at neighborhoods and boroughs levels will guide the employee through neighbourhoods and boroughs of chosen city.

List of neighborhoods

Let us scrape data from various sources and build list of neighborhoods for each city.

Toronto, Ontario

State City Borough Neighborhood
0 Ontario Toronto North York Parkwoods
1 Ontario Toronto North York Victoria Village
2 Ontario Toronto Downtown Toronto Harbourfront
3 Ontario Toronto North York Lawrence Heights
4 Ontario Toronto North York Lawrence Manor

New York, New York

State City Borough Neighborhood
0 New York New York Bronx Melrose
1 New York New York Bronx Mott Haven
2 New York New York Bronx Port Morris
3 New York New York Bronx Hunts Point
4 New York New York Bronx Longwood

Chicago, Illinois

State City Borough Neighborhood
0 Illinois Chicago Far North Side Loyola
1 Illinois Chicago Far North Side Rogers Park
2 Illinois Chicago Far North Side Nortown
3 Illinois Chicago Far North Side Peterson Park
4 Illinois Chicago Far North Side Rosehill

Dallas, Texas

State City Borough Neighborhood
0 Texas Dallas Downtown Dallas Baylor District
1 Texas Dallas Downtown Dallas The Cedars
2 Texas Dallas Downtown Dallas Civic Center District
3 Texas Dallas Downtown Dallas Dallas Arts District
4 Texas Dallas Downtown Dallas Dallas Farmers Market

Geolocation data

Let us retrieve geolocation data for each city's neighbourhood and visualize them on maps.

Toronto, Ontario

New York, New York

Chicago, Illinois

Dallas, Texas

Venues exploration

Let us explore each city's neighborhood and find out venues there.

Toronto, Ontario

State City Borough Neighborhood Neighborhood Latitude Neighborhood Longitude Venue Venue Latitude Venue Longitude Venue Category
0 Ontario Toronto North York Parkwoods 43.752777 -79.326440 Brookbanks Park 43.751976 -79.332140 Park
1 Ontario Toronto North York Parkwoods 43.752777 -79.326440 Ranchdale Park 43.751388 -79.322138 Park
2 Ontario Toronto North York Parkwoods 43.752777 -79.326440 S&A Tile & Flooring Services 43.753928 -79.320635 Furniture / Home Store
3 Ontario Toronto North York Victoria Village 43.735735 -79.312418 Jatujak 43.736208 -79.307668 Thai Restaurant
4 Ontario Toronto North York Victoria Village 43.735735 -79.312418 Artistic Nails 43.736120 -79.308080 Spa

New York, New York

State City Borough Neighborhood Neighborhood Latitude Neighborhood Longitude Venue Venue Latitude Venue Longitude Venue Category
0 New York New York Bronx Melrose 40.824545 -73.910414 Porto Salvo 40.823887 -73.912910 Italian Restaurant
1 New York New York Bronx Melrose 40.824545 -73.910414 Perry Coffee Shop. 40.823181 -73.910928 Diner
2 New York New York Bronx Melrose 40.824545 -73.910414 Cinco de Mayo 40.822674 -73.911592 Mexican Restaurant
3 New York New York Bronx Melrose 40.824545 -73.910414 Old Bronx Courthouse 40.822894 -73.909565 Art Gallery
4 New York New York Bronx Melrose 40.824545 -73.910414 Popeyes Louisiana Kitchen 40.824605 -73.909819 Fried Chicken Joint

Chicago, Illinois

State City Borough Neighborhood Neighborhood Latitude Neighborhood Longitude Venue Venue Latitude Venue Longitude Venue Category
0 Illinois Chicago Far North Side Loyola 41.998948 -87.658259 7-Eleven 41.998363 -87.659713 Convenience Store
1 Illinois Chicago Far North Side Loyola 41.998948 -87.658259 Chase Bank 41.998812 -87.660245 Bank
2 Illinois Chicago Far North Side Loyola 41.998948 -87.658259 Starbucks 41.997897 -87.660862 Coffee Shop
3 Illinois Chicago Far North Side Loyola 41.998948 -87.658259 Nori 41.998076 -87.662075 Sushi Restaurant
4 Illinois Chicago Far North Side Loyola 41.998948 -87.658259 Potbelly Sandwich Shop 42.000031 -87.660905 Sandwich Place

Dallas, Texas

State City Borough Neighborhood Neighborhood Latitude Neighborhood Longitude Venue Venue Latitude Venue Longitude Venue Category
0 Texas Dallas Downtown Dallas Baylor District 32.776282 -96.797367 Pioneer Plaza 32.776616 -96.801539 Plaza
1 Texas Dallas Downtown Dallas Baylor District 32.776282 -96.797367 Spice in the City 32.780014 -96.797829 Indian Restaurant
2 Texas Dallas Downtown Dallas Baylor District 32.776282 -96.797367 AT&T 32.779811 -96.798750 Mobile Phone Shop
3 Texas Dallas Downtown Dallas Baylor District 32.776282 -96.797367 Aloft Dallas Downtown 32.777186 -96.801110 Hotel
4 Texas Dallas Downtown Dallas Baylor District 32.776282 -96.797367 Weekend 32.780309 -96.798191 Coffee Shop

Unexplorable neighborhoods

Let us see each city's neighborhood, which can be taken under consideration in very limited way, because there is no relevant information available.

Number of unexplorable neighborhoods: 20
Unexplorable neighborhoods
State City
Illinois Chicago 3
New York New York 1
Ontario Toronto 1
Texas Dallas 15

Methodology

To find similarity between cities, their boroughs and neighborhoods we will analyze presence of venues in their neighborhoods grouped by venue categories. We will use k-means algorithm to find out similarity between neighborhoods and fit it with frequencies of presence venue categories in neighborhoods.

To compare objects we will use indicators as follow:

  • neighborhoods: cluster labels which are the output of k-means algorithm
  • boroughs: we will use frequencies of presence of cluster labels in neighborhoods as their features and calculate distance between each other using them
  • cities: we will do the same as for neighborhoods

We will report results of the analysis for objects as follow:

  • neighborhoods: relevant use city maps marked with colored points relevant to cluster labels of each neighborhood, the points show neighborhood locations
  • boroughs: dataframe sorted by distance and city, smaller distance value means more similar borough
  • city: ordered list of cities, the most similar at the top of

Analysis

Venues in neighborhoods

One hot encoding of venue categories

Let us see how many unique venue categories were found.

There are 514 uniques venue categories.

Let us use one hot encoding and build relevant dataframe.

State City Borough Neighborhood ATM Accessories Store Adult Boutique Advertising Agency Afghan Restaurant African Restaurant Airport Airport Food Court Airport Gate Airport Lounge Airport Service Airport Terminal American Restaurant Amphitheater Animal Shelter Antique Shop Aquarium Arcade Arepa Restaurant Argentinian Restaurant Art Gallery Art Museum Arts & Crafts Store Arts & Entertainment Asian Restaurant Athletics & Sports Auditorium Australian Restaurant Austrian Restaurant Auto Dealership Auto Garage Auto Workshop Automotive Shop BBQ Joint Baby Store Bagel Shop Bakery Bank Bar Baseball Field Baseball Stadium Basketball Court Basketball Stadium Beach Beach Bar Bed & Breakfast Beer Bar Beer Garden Beer Store Belgian Restaurant Big Box Store Bike Rental / Bike Share Bike Shop Bike Trail Bistro Board Shop Boat Rental Boat or Ferry Bookstore Botanical Garden Boutique Bowling Alley Boxing Gym Brazilian Restaurant Breakfast Spot Brewery Bridal Shop Bridge Bubble Tea Shop Buffet Building Burger Joint Burrito Place Bus Line Bus Station Bus Stop Business Service Butcher Cafeteria Café Cajun / Creole Restaurant Cambodian Restaurant Camera Store Campground Candy Store Cantonese Restaurant Caribbean Restaurant Carpet Store Caucasian Restaurant Cemetery Check Cashing Service Cheese Shop Child Care Service Chinese Restaurant Chiropractor Chocolate Shop Christmas Market Church Circus Climbing Gym Clothing Store Club House Cocktail Bar Coffee Shop College Academic Building College Arts Building College Auditorium College Bookstore College Cafeteria College Gym College Quad College Rec Center College Soccer Field College Theater College Track Colombian Restaurant Comedy Club Comfort Food Restaurant Comic Shop Community Center Concert Hall Construction & Landscaping Convenience Store Cooking School Cosmetics Shop Costume Shop Coworking Space Creperie Cuban Restaurant Cultural Center Cupcake Shop Curling Ice Currency Exchange Cycle Studio Czech Restaurant Dance Studio Daycare Deli / Bodega Dentist's Office Department Store Design Studio Dessert Shop Dim Sum Restaurant Diner Disc Golf Discount Store Distillery Dive Bar Doctor's Office Dog Run Doner Restaurant Donut Shop Dosa Place Drugstore Dry Cleaner Dumpling Restaurant Dutch Restaurant Duty-free Shop Eastern European Restaurant Egyptian Restaurant Electronics Store Elementary School Empanada Restaurant English Restaurant Entertainment Service Ethiopian Restaurant Event Service Event Space Exhibit Eye Doctor Fabric Shop Factory Falafel Restaurant Farm Farmers Market Fast Food Restaurant Festival Field Filipino Restaurant Film Studio Financial or Legal Service Fish & Chips Shop Fish Market Fishing Store Flea Market Flower Shop Fondue Restaurant Food Food & Drink Shop Food Court Food Service Food Stand Food Truck Football Stadium Fountain Frame Store French Restaurant Fried Chicken Joint Frozen Yogurt Shop Fruit & Vegetable Store Furniture / Home Store Gaming Cafe Garden Garden Center Gas Station Gastropub Gay Bar General College & University General Entertainment General Travel German Restaurant Gift Shop Gluten-free Restaurant Golf Course Golf Driving Range Gourmet Shop Greek Restaurant Grocery Store Gym Gym / Fitness Center Gym Pool Gymnastics Gym Halal Restaurant Harbor / Marina Hardware Store Hawaiian Restaurant Health & Beauty Service Health Food Store Heliport Herbs & Spices Store High School Himalayan Restaurant Historic Site History Museum Hobby Shop Hockey Arena Home Service Hong Kong Restaurant Hookah Bar Hospital Hostel Hot Dog Joint Hotel Hotel Bar Hotel Pool Hotpot Restaurant IT Services Ice Cream Shop Indian Chinese Restaurant Indian Restaurant Indie Movie Theater Indie Theater Indonesian Restaurant Indoor Play Area Insurance Office Intersection Irish Pub Island Israeli Restaurant Italian Restaurant Japanese Curry Restaurant Japanese Restaurant Jazz Club Jewelry Store Jewish Restaurant Juice Bar Karaoke Bar Kebab Restaurant Kids Store Kitchen Supply Store Korean Restaurant Kosher Restaurant Lake Laser Tag Latin American Restaurant Laundromat Laundry Service Lawyer Leather Goods Store Lebanese Restaurant Library Light Rail Station Lighthouse Lingerie Store Liquor Store Locksmith Lounge Luggage Store Mac & Cheese Joint Malay Restaurant Marijuana Dispensary Market Martial Arts Dojo Massage Studio Mattress Store Medical Center Medical Supply Store Mediterranean Restaurant Memorial Site Men's Store Metro Station Mexican Restaurant Middle Eastern Restaurant Mini Golf Miscellaneous Shop Mobile Phone Shop Modern European Restaurant Modern Greek Restaurant Molecular Gastronomy Restaurant Monument / Landmark Moroccan Restaurant Motel Motorcycle Shop Movie Theater Moving Target Multiplex Museum Music School Music Store Music Venue Nail Salon Nature Preserve Neighborhood VC New American Restaurant Newsstand Nightclub Non-Profit Noodle House North Indian Restaurant Office Opera House Optical Shop Organic Grocery Other Great Outdoors Other Nightlife Other Repair Shop Outdoor Sculpture Outdoor Supply Store Outdoors & Recreation Outlet Store Paella Restaurant Pakistani Restaurant Paper / Office Supplies Store Park Parking Pastry Shop Pawn Shop Pedestrian Plaza Peking Duck Restaurant Performing Arts Venue Persian Restaurant Peruvian Restaurant Pet Café Pet Service Pet Store Pharmacy Photography Lab Photography Studio Piano Bar Pie Shop Pier Pilates Studio Pizza Place Planetarium Platform Playground Plaza Poke Place Polish Restaurant Pool Pool Hall Portuguese Restaurant Poutine Place Print Shop Pub Public Art Racetrack Ramen Restaurant Record Shop Recording Studio Recreation Center Rental Car Location Rental Service Residential Building (Apartment / Condo) Resort Rest Area Restaurant River Road Rock Climbing Spot Rock Club Roller Rink Roof Deck Russian Restaurant Sake Bar Salad Place Salon / Barbershop Salvadoran Restaurant Sandwich Place Scandinavian Restaurant Scenic Lookout School Science Museum Sculpture Garden Seafood Restaurant Shabu-Shabu Restaurant Shanghai Restaurant Shipping Store Shoe Repair Shoe Store Shop & Service Shopping Mall Shopping Plaza Skate Park Skating Rink Ski Lodge Ski Shop Smoke Shop Smoothie Shop Snack Place Soba Restaurant Soccer Field Soccer Stadium Social Club Soup Place South American Restaurant South Indian Restaurant Southern / Soul Food Restaurant Souvenir Shop Souvlaki Shop Spa Spanish Restaurant Speakeasy Sporting Goods Shop Sports Bar Sports Club Sri Lankan Restaurant Stables Stadium Stationery Store Steakhouse Storage Facility Street Art Street Food Gathering Strip Club Supermarket Supplement Shop Surf Spot Sushi Restaurant Swiss Restaurant Synagogue Syrian Restaurant Szechuan Restaurant Taco Place Tailor Shop Taiwanese Restaurant Tanning Salon Tapas Restaurant Tattoo Parlor Taxi Stand Tea Room Tech Startup Tennis Court Tennis Stadium Tex-Mex Restaurant Thai Restaurant Theater Theme Park Theme Park Ride / Attraction Theme Restaurant Thrift / Vintage Store Tibetan Restaurant Tiki Bar Toll Plaza Tour Provider Tourist Information Center Toy / Game Store Track Track Stadium Trade School Trail Train Train Station Tram Station Travel Lounge Tree Tunnel Turkish Restaurant Udon Restaurant Ukrainian Restaurant University Used Bookstore Vacation Rental Vape Store Varenyky restaurant Vegetarian / Vegan Restaurant Venezuelan Restaurant Veterinarian Video Game Store Video Store Vietnamese Restaurant Volleyball Court Warehouse Store Waste Facility Waterfront Weight Loss Center Whisky Bar Wine Bar Wine Shop Wings Joint Women's Store Yoga Studio Zoo Zoo Exhibit
0 New York New York Bronx Melrose 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 New York New York Bronx Melrose 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 New York New York Bronx Melrose 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 New York New York Bronx Melrose 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 New York New York Bronx Melrose 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Frequencies of occurency of venue categories

Let us group rows by city's neighbourhood and by taking the mean of the frequency of occurrence of each venue category.

State City Borough Neighborhood ATM Accessories Store Adult Boutique Advertising Agency Afghan Restaurant African Restaurant Airport Airport Food Court Airport Gate Airport Lounge Airport Service Airport Terminal American Restaurant Amphitheater Animal Shelter Antique Shop Aquarium Arcade Arepa Restaurant Argentinian Restaurant Art Gallery Art Museum Arts & Crafts Store Arts & Entertainment Asian Restaurant Athletics & Sports Auditorium Australian Restaurant Austrian Restaurant Auto Dealership Auto Garage Auto Workshop Automotive Shop BBQ Joint Baby Store Bagel Shop Bakery Bank Bar Baseball Field Baseball Stadium Basketball Court Basketball Stadium Beach Beach Bar Bed & Breakfast Beer Bar Beer Garden Beer Store Belgian Restaurant Big Box Store Bike Rental / Bike Share Bike Shop Bike Trail Bistro Board Shop Boat Rental Boat or Ferry Bookstore Botanical Garden Boutique Bowling Alley Boxing Gym Brazilian Restaurant Breakfast Spot Brewery Bridal Shop Bridge Bubble Tea Shop Buffet Building Burger Joint Burrito Place Bus Line Bus Station Bus Stop Business Service Butcher Cafeteria Café Cajun / Creole Restaurant Cambodian Restaurant Camera Store Campground Candy Store Cantonese Restaurant Caribbean Restaurant Carpet Store Caucasian Restaurant Cemetery Check Cashing Service Cheese Shop Child Care Service Chinese Restaurant Chiropractor Chocolate Shop Christmas Market Church Circus Climbing Gym Clothing Store Club House Cocktail Bar Coffee Shop College Academic Building College Arts Building College Auditorium College Bookstore College Cafeteria College Gym College Quad College Rec Center College Soccer Field College Theater College Track Colombian Restaurant Comedy Club Comfort Food Restaurant Comic Shop Community Center Concert Hall Construction & Landscaping Convenience Store Cooking School Cosmetics Shop Costume Shop Coworking Space Creperie Cuban Restaurant Cultural Center Cupcake Shop Curling Ice Currency Exchange Cycle Studio Czech Restaurant Dance Studio Daycare Deli / Bodega Dentist's Office Department Store Design Studio Dessert Shop Dim Sum Restaurant Diner Disc Golf Discount Store Distillery Dive Bar Doctor's Office Dog Run Doner Restaurant Donut Shop Dosa Place Drugstore Dry Cleaner Dumpling Restaurant Dutch Restaurant Duty-free Shop Eastern European Restaurant Egyptian Restaurant Electronics Store Elementary School Empanada Restaurant English Restaurant Entertainment Service Ethiopian Restaurant Event Service Event Space Exhibit Eye Doctor Fabric Shop Factory Falafel Restaurant Farm Farmers Market Fast Food Restaurant Festival Field Filipino Restaurant Film Studio Financial or Legal Service Fish & Chips Shop Fish Market Fishing Store Flea Market Flower Shop Fondue Restaurant Food Food & Drink Shop Food Court Food Service Food Stand Food Truck Football Stadium Fountain Frame Store French Restaurant Fried Chicken Joint Frozen Yogurt Shop Fruit & Vegetable Store Furniture / Home Store Gaming Cafe Garden Garden Center Gas Station Gastropub Gay Bar General College & University General Entertainment General Travel German Restaurant Gift Shop Gluten-free Restaurant Golf Course Golf Driving Range Gourmet Shop Greek Restaurant Grocery Store Gym Gym / Fitness Center Gym Pool Gymnastics Gym Halal Restaurant Harbor / Marina Hardware Store Hawaiian Restaurant Health & Beauty Service Health Food Store Heliport Herbs & Spices Store High School Himalayan Restaurant Historic Site History Museum Hobby Shop Hockey Arena Home Service Hong Kong Restaurant Hookah Bar Hospital Hostel Hot Dog Joint Hotel Hotel Bar Hotel Pool Hotpot Restaurant IT Services Ice Cream Shop Indian Chinese Restaurant Indian Restaurant Indie Movie Theater Indie Theater Indonesian Restaurant Indoor Play Area Insurance Office Intersection Irish Pub Island Israeli Restaurant Italian Restaurant Japanese Curry Restaurant Japanese Restaurant Jazz Club Jewelry Store Jewish Restaurant Juice Bar Karaoke Bar Kebab Restaurant Kids Store Kitchen Supply Store Korean Restaurant Kosher Restaurant Lake Laser Tag Latin American Restaurant Laundromat Laundry Service Lawyer Leather Goods Store Lebanese Restaurant Library Light Rail Station Lighthouse Lingerie Store Liquor Store Locksmith Lounge Luggage Store Mac & Cheese Joint Malay Restaurant Marijuana Dispensary Market Martial Arts Dojo Massage Studio Mattress Store Medical Center Medical Supply Store Mediterranean Restaurant Memorial Site Men's Store Metro Station Mexican Restaurant Middle Eastern Restaurant Mini Golf Miscellaneous Shop Mobile Phone Shop Modern European Restaurant Modern Greek Restaurant Molecular Gastronomy Restaurant Monument / Landmark Moroccan Restaurant Motel Motorcycle Shop Movie Theater Moving Target Multiplex Museum Music School Music Store Music Venue Nail Salon Nature Preserve Neighborhood VC New American Restaurant Newsstand Nightclub Non-Profit Noodle House North Indian Restaurant Office Opera House Optical Shop Organic Grocery Other Great Outdoors Other Nightlife Other Repair Shop Outdoor Sculpture Outdoor Supply Store Outdoors & Recreation Outlet Store Paella Restaurant Pakistani Restaurant Paper / Office Supplies Store Park Parking Pastry Shop Pawn Shop Pedestrian Plaza Peking Duck Restaurant Performing Arts Venue Persian Restaurant Peruvian Restaurant Pet Café Pet Service Pet Store Pharmacy Photography Lab Photography Studio Piano Bar Pie Shop Pier Pilates Studio Pizza Place Planetarium Platform Playground Plaza Poke Place Polish Restaurant Pool Pool Hall Portuguese Restaurant Poutine Place Print Shop Pub Public Art Racetrack Ramen Restaurant Record Shop Recording Studio Recreation Center Rental Car Location Rental Service Residential Building (Apartment / Condo) Resort Rest Area Restaurant River Road Rock Climbing Spot Rock Club Roller Rink Roof Deck Russian Restaurant Sake Bar Salad Place Salon / Barbershop Salvadoran Restaurant Sandwich Place Scandinavian Restaurant Scenic Lookout School Science Museum Sculpture Garden Seafood Restaurant Shabu-Shabu Restaurant Shanghai Restaurant Shipping Store Shoe Repair Shoe Store Shop & Service Shopping Mall Shopping Plaza Skate Park Skating Rink Ski Lodge Ski Shop Smoke Shop Smoothie Shop Snack Place Soba Restaurant Soccer Field Soccer Stadium Social Club Soup Place South American Restaurant South Indian Restaurant Southern / Soul Food Restaurant Souvenir Shop Souvlaki Shop Spa Spanish Restaurant Speakeasy Sporting Goods Shop Sports Bar Sports Club Sri Lankan Restaurant Stables Stadium Stationery Store Steakhouse Storage Facility Street Art Street Food Gathering Strip Club Supermarket Supplement Shop Surf Spot Sushi Restaurant Swiss Restaurant Synagogue Syrian Restaurant Szechuan Restaurant Taco Place Tailor Shop Taiwanese Restaurant Tanning Salon Tapas Restaurant Tattoo Parlor Taxi Stand Tea Room Tech Startup Tennis Court Tennis Stadium Tex-Mex Restaurant Thai Restaurant Theater Theme Park Theme Park Ride / Attraction Theme Restaurant Thrift / Vintage Store Tibetan Restaurant Tiki Bar Toll Plaza Tour Provider Tourist Information Center Toy / Game Store Track Track Stadium Trade School Trail Train Train Station Tram Station Travel Lounge Tree Tunnel Turkish Restaurant Udon Restaurant Ukrainian Restaurant University Used Bookstore Vacation Rental Vape Store Varenyky restaurant Vegetarian / Vegan Restaurant Venezuelan Restaurant Veterinarian Video Game Store Video Store Vietnamese Restaurant Volleyball Court Warehouse Store Waste Facility Waterfront Weight Loss Center Whisky Bar Wine Bar Wine Shop Wings Joint Women's Store Yoga Studio Zoo Zoo Exhibit
0 Illinois Chicago Central Cabrini–Green 0.0 0.0 0.0 0.0 0.0 0.041667 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.041667 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.041667 0.041667 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.083333 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.041667 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.041667 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.041667 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.041667 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.041667 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.083333 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.041667 0.0 0.0 0.0 0.000000 0.041667 0.0 0.0 0.041667 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.041667 0.0 0.0 0.0 0.000000 0.041667 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.041667 0.0 0.0 0.0 0.0 0.0 0.0 0.041667 0.0 0.0 0.000000 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.041667 0.0 0.000000 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0 0.041667 0.0 0.000000 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.041667 0.041667 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0
1 Illinois Chicago Central Central Station 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.035714 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.017857 0.017857 0.017857 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.017857 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.017857 0.000000 0.0 0.0 0.0 0.0 0.0 0.035714 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.017857 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.017857 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.017857 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.017857 0.0 0.0 0.0 0.0 0.000000 0.017857 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017857 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017857 0.0 0.0 0.0 0.0 0.0 0.017857 0.017857 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.035714 0.196429 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.017857 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.017857 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017857 0.0 0.000000 0.053571 0.0 0.017857 0.017857 0.0 0.0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.017857 0.000000 0.000000 0.0 0.0 0.0 0.017857 0.017857 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017857 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.035714 0.0 0.0 0.017857 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.000000 0.017857 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.017857 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017857 0.0 0.017857 0.0 0.0 0.0 0.000000 0.017857 0.000000 0.0 0.0 0.000000 0.0 0.000000 0.0 0.000000 0.0 0.017857 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.017857 0.0 0.0 0.0 0.0 0.0 0.017857 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.017857 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.017857 0.0 0.0
2 Illinois Chicago Central Dearborn Park 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.023256 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.011628 0.0 0.0 0.0 0.011628 0.000000 0.0 0.0 0.0 0.0 0.0 0.034884 0.0 0.0 0.023256 0.0 0.0 0.0 0.0 0.000000 0.011628 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.034884 0.0 0.000000 0.058140 0.0 0.011628 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.011628 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.0 0.0 0.011628 0.011628 0.0 0.011628 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.011628 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.023256 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.034884 0.023256 0.046512 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.000000 0.0 0.0 0.0 0.0 0.011628 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.011628 0.0 0.023256 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.023256 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.011628 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.023256 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.0 0.023256 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.011628 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.011628 0.011628 0.0 0.011628 0.000000 0.0 0.0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.023256 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.000000 0.011628 0.0 0.0 0.0 0.0 0.0 0.0 0.034884 0.0 0.0 0.011628 0.0 0.0 0.0 0.000000 0.0 0.011628 0.0 0.0 0.011628 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.046512 0.0 0.0 0.0 0.000000 0.011628 0.000000 0.0 0.0 0.011628 0.0 0.000000 0.0 0.011628 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.011628 0.011628 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.0 0.0 0.011628 0.000000 0.0 0.011628 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011628 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.011628 0.023256 0.0 0.0
3 Illinois Chicago Central Gold Coast 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.041667 0.0 0.0 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013889 0.000000 0.0 0.000000 0.013889 0.041667 0.000000 0.0 0.0 0.0 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.013889 0.0 0.000000 0.0 0.0 0.0 0.013889 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.041667 0.000000 0.0 0.0 0.0 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013889 0.0 0.013889 0.041667 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.013889 0.013889 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.041667 0.027778 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.013889 0.0 0.0 0.0 0.0 0.000000 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.027778 0.013889 0.0 0.0 0.0 0.013889 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.069444 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.027778 0.0 0.0 0.0 0.0 0.0 0.0 0.027778 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.013889 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.013889 0.0 0.013889 0.013889 0.0 0.0 0.0 0.0 0.013889 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.013889 0.0 0.0 0.013889 0.0 0.0 0.0 0.027778 0.0 0.000000 0.0 0.0 0.013889 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.027778 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013889 0.0 0.013889 0.0 0.0 0.0 0.013889 0.000000 0.000000 0.0 0.0 0.013889 0.0 0.013889 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.041667 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013889 0.0 0.013889 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.013889 0.000000 0.027778 0.0 0.0
4 Illinois Chicago Central Goose Island 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.058824 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.058824 0.0 0.000000 0.000000 0.058824 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.000000 0.058824 0.0 0.0 0.0 0.0 0.0 0.058824 0.0 0.0 0.058824 0.0 0.0 0.0 0.0 0.058824 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.058824 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.058824 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.058824 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.058824 0.000000 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.058824 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.058824 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.058824 0.0 0.0 0.0 0.000000 0.000000 0.058824 0.0 0.0 0.000000 0.0 0.000000 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.058824 0.000000 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.058824 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0

Top most common venue categories

Let us find out the most common 10 venue categories for each city's neighbourhood.

State City Borough Neighborhood 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue
0 Illinois Chicago Central Cabrini–Green Lounge Coffee Shop Supermarket Fast Food Restaurant Bakery Outdoors & Recreation Gym Shipping Store Mediterranean Restaurant Big Box Store
1 Illinois Chicago Central Central Station History Museum Museum American Restaurant Pizza Place Burger Joint Historic Site Gift Shop Breakfast Spot Dog Run Sushi Restaurant
2 Illinois Chicago Central Dearborn Park Coffee Shop Gym / Fitness Center Sandwich Place Pizza Place Clothing Store Grocery Store Burger Joint Italian Restaurant Gym Asian Restaurant
3 Illinois Chicago Central Gold Coast Italian Restaurant Gym Bar Café Steakhouse American Restaurant Coffee Shop Massage Studio Gym / Fitness Center Hotel
4 Illinois Chicago Central Goose Island Burger Joint Café Baby Store Outdoor Supply Store Seafood Restaurant Pet Store Donut Shop Arts & Entertainment Sandwich Place Kitchen Supply Store

Clustering of neighborhoods

Optimal number of centroids

Let us use k-means algorithm to find out optimal number of centroids for clustering city's neighborhoods.

k-means algorithm is known to fluctuation because of randomly chosen centroids and presence of local minimas. To reduce that fluctuation we execute it few times using different random state. At the end one random state will be chosen, that one which gives the highest Silhouette score for analyzed data.

It seems, that all neighborhoods are rather similar each to other. The best Silhouette score is 0.5455, higher than 0.5, all clusters have Silhouette score for their data points higher than that one for entire data. So, for the comparison we choose 3 clusters, even the cluster labelled with 0 is signigicantly bigger than other ones.

The Elbow method does not clearly show the proper clusters number, so we will follow results given by Silhouette method.

Labelling of neighborhoods

Let us apply clustering to all neighbourhoods. Note, that there is some unexplorable neighborhoods, which were mentioned earlier, they will be added and labelled with -1.

Toronto, Ontario

New York, New York

Chicago, Illinois

Dallas, Texas

Similarity of neighborhoods

Let us compare numer of city's neighborhoods according to labelling they get.

Cluster Label -1 0 1 2
State City Borough Neighborhood
Illinois Chicago Central Cabrini–Green 0 1 0 0
Central Station 0 1 0 0
Dearborn Park 0 1 0 0
Gold Coast 0 1 0 0
Goose Island 0 1 0 0
Loop 0 1 0 0
Magnificent Mile 0 1 0 0
Museum Campus 0 1 0 0
Near North Side 0 1 0 0
New Eastside 0 1 0 0
Old Town 0 1 0 0
Prairie Avenue Historic District 0 1 0 0
Printer's Row 0 1 0 0
River North 0 1 0 0
South Loop 0 1 0 0
Streeterville 0 1 0 0
Far North Side Albany Park 0 1 0 0
Andersonville 0 1 0 0
Big Oaks 0 1 0 0
Bowmanville 0 1 0 0
Budlong Woods 0 1 0 0
Buena Park 0 1 0 0
Clarendon Park 0 1 0 0
Edgebrook 0 1 0 0
Edgewater 0 1 0 0
Edgewater Beach 0 1 0 0
Edgewater Glen 0 1 0 0
Edison Park 0 1 0 0
Forest Glen 0 1 0 0
Gladstone Park 0 1 0 0
Hollywood Park 0 1 0 0
Jefferson Park 0 1 0 0
Lakewood / Balmoral 0 1 0 0
Lincoln Square 0 1 0 0
Loyola 0 1 0 0
Margate Park 0 1 0 0
Mayfair 0 1 0 0
New Chinatown 0 1 0 0
North Mayfair 0 1 0 0
North Park 0 1 0 0
Nortown 0 1 0 0
Norwood Park East 0 1 0 0
Norwood Park West 0 1 0 0
O'Hare 0 1 0 0
Old Edgebrook 0 1 0 0
Old Norwood 0 1 0 0
Oriole Park 0 1 0 0
Peterson Park 0 1 0 0
Ravenswood 0 1 0 0
Ravenswood Gardens 0 1 0 0

Similarity of boroughs

Let us compare numer of city's neighborhoods according to labelling they get grouping by city's boroughs.

Cluster Label -1 0 1 2
State City Borough
Illinois Chicago Central 0 16 0 0
Far North Side 0 47 0 0
Far Southeast Side 3 24 0 0
Far Southwest Side 0 21 0 0
North Side 0 27 0 0
Northwest Side 0 20 0 0
South Side 0 28 0 0
Southwest Side 0 23 0 0
West Side 0 37 0 0
New York New York Bronx 0 60 0 0
Brooklyn 0 79 0 0
Manhattan 0 47 0 0
Queens 0 86 0 0
Staten Island 1 55 0 0
Ontario Toronto Central Toronto 0 17 0 0
Downtown Toronto 0 37 0 0
East Toronto 0 7 0 0
East York 0 6 0 0
Etobicoke 1 43 0 0
Mississauga 0 1 0 0
North York 0 35 3 0
Queen's Park 0 1 0 0
Scarborough 0 37 0 0
West Toronto 0 13 0 0
York 0 9 0 0
Texas Dallas Downtown Dallas 0 15 0 0
East Dallas 1 33 0 0
Fair Park 0 12 0 0
Far North Dallas 1 6 0 0
Far South Dallas 0 3 0 0
North Dallas 1 7 0 0
Northeast Dallas 6 36 0 0
Northwest Dallas 0 7 0 0
Oak Cliff Area 0 19 0 0
Oak Lawn 0 11 0 0
Old East Dallas 1 6 0 0
Redbird 1 3 0 0
South Central Dallas 0 2 0 0
Southeast Dallas 2 10 0 9
West Dallas 2 9 0 0

Similarity of cities

Let us compare numer of city's neighborhoods according to labelling they get grouping by cities.

Cluster Label -1 0 1 2
State City
Illinois Chicago 3 243 0 0
New York New York 1 327 0 0
Ontario Toronto 1 206 3 0
Texas Dallas 15 179 0 9

Results

Comparison

Let us compare similarity of objects by calculating distance between them. Each object features, which are taken for distance calculation, are its cluster labels.

Cities level

Let us compare similarity of cities.

Cluster Label -1 0 1 2 Distance
State City
Ontario Toronto 0.004762 0.980952 0.014286 0.000000 0.000000
Illinois Chicago 0.012195 0.987805 0.000000 0.000000 0.017501
New York New York 0.003049 0.996951 0.000000 0.000000 0.021517
Texas Dallas 0.073892 0.881773 0.000000 0.044335 0.129557

List of cities in the most similar to Toronto, Ontario, which the employee comes from:

  1. Chicago, Illinois
  2. New York, New York
  3. Dallas, Texas

Boroughs level

Let us compare similarity of city's boroughs.

Employee State Employee City Employee Borough State City Borough Distance
0 Ontario Toronto Central Toronto Illinois Chicago Central 0.0
29 Ontario Toronto Downtown Toronto Illinois Chicago Central 0.0
58 Ontario Toronto East Toronto Illinois Chicago Central 0.0
87 Ontario Toronto East York Illinois Chicago Central 0.0
145 Ontario Toronto Mississauga Illinois Chicago Central 0.0
203 Ontario Toronto Queen's Park Illinois Chicago Central 0.0
232 Ontario Toronto Scarborough Illinois Chicago Central 0.0
261 Ontario Toronto West Toronto Illinois Chicago Central 0.0
290 Ontario Toronto York Illinois Chicago Central 0.0
1 Ontario Toronto Central Toronto Illinois Chicago Far North Side 0.0
30 Ontario Toronto Downtown Toronto Illinois Chicago Far North Side 0.0
59 Ontario Toronto East Toronto Illinois Chicago Far North Side 0.0
88 Ontario Toronto East York Illinois Chicago Far North Side 0.0
146 Ontario Toronto Mississauga Illinois Chicago Far North Side 0.0
204 Ontario Toronto Queen's Park Illinois Chicago Far North Side 0.0
233 Ontario Toronto Scarborough Illinois Chicago Far North Side 0.0
262 Ontario Toronto West Toronto Illinois Chicago Far North Side 0.0
291 Ontario Toronto York Illinois Chicago Far North Side 0.0
3 Ontario Toronto Central Toronto Illinois Chicago Far Southwest Side 0.0
32 Ontario Toronto Downtown Toronto Illinois Chicago Far Southwest Side 0.0
61 Ontario Toronto East Toronto Illinois Chicago Far Southwest Side 0.0
90 Ontario Toronto East York Illinois Chicago Far Southwest Side 0.0
148 Ontario Toronto Mississauga Illinois Chicago Far Southwest Side 0.0
206 Ontario Toronto Queen's Park Illinois Chicago Far Southwest Side 0.0
235 Ontario Toronto Scarborough Illinois Chicago Far Southwest Side 0.0
264 Ontario Toronto West Toronto Illinois Chicago Far Southwest Side 0.0
293 Ontario Toronto York Illinois Chicago Far Southwest Side 0.0
4 Ontario Toronto Central Toronto Illinois Chicago North Side 0.0
33 Ontario Toronto Downtown Toronto Illinois Chicago North Side 0.0
62 Ontario Toronto East Toronto Illinois Chicago North Side 0.0
91 Ontario Toronto East York Illinois Chicago North Side 0.0
149 Ontario Toronto Mississauga Illinois Chicago North Side 0.0
207 Ontario Toronto Queen's Park Illinois Chicago North Side 0.0
236 Ontario Toronto Scarborough Illinois Chicago North Side 0.0
265 Ontario Toronto West Toronto Illinois Chicago North Side 0.0
294 Ontario Toronto York Illinois Chicago North Side 0.0
5 Ontario Toronto Central Toronto Illinois Chicago Northwest Side 0.0
34 Ontario Toronto Downtown Toronto Illinois Chicago Northwest Side 0.0
63 Ontario Toronto East Toronto Illinois Chicago Northwest Side 0.0
92 Ontario Toronto East York Illinois Chicago Northwest Side 0.0
150 Ontario Toronto Mississauga Illinois Chicago Northwest Side 0.0
208 Ontario Toronto Queen's Park Illinois Chicago Northwest Side 0.0
237 Ontario Toronto Scarborough Illinois Chicago Northwest Side 0.0
266 Ontario Toronto West Toronto Illinois Chicago Northwest Side 0.0
295 Ontario Toronto York Illinois Chicago Northwest Side 0.0
6 Ontario Toronto Central Toronto Illinois Chicago South Side 0.0
35 Ontario Toronto Downtown Toronto Illinois Chicago South Side 0.0
64 Ontario Toronto East Toronto Illinois Chicago South Side 0.0
93 Ontario Toronto East York Illinois Chicago South Side 0.0
151 Ontario Toronto Mississauga Illinois Chicago South Side 0.0
209 Ontario Toronto Queen's Park Illinois Chicago South Side 0.0
238 Ontario Toronto Scarborough Illinois Chicago South Side 0.0
267 Ontario Toronto West Toronto Illinois Chicago South Side 0.0
296 Ontario Toronto York Illinois Chicago South Side 0.0
7 Ontario Toronto Central Toronto Illinois Chicago Southwest Side 0.0
36 Ontario Toronto Downtown Toronto Illinois Chicago Southwest Side 0.0
65 Ontario Toronto East Toronto Illinois Chicago Southwest Side 0.0
94 Ontario Toronto East York Illinois Chicago Southwest Side 0.0
152 Ontario Toronto Mississauga Illinois Chicago Southwest Side 0.0
210 Ontario Toronto Queen's Park Illinois Chicago Southwest Side 0.0
239 Ontario Toronto Scarborough Illinois Chicago Southwest Side 0.0
268 Ontario Toronto West Toronto Illinois Chicago Southwest Side 0.0
297 Ontario Toronto York Illinois Chicago Southwest Side 0.0
8 Ontario Toronto Central Toronto Illinois Chicago West Side 0.0
37 Ontario Toronto Downtown Toronto Illinois Chicago West Side 0.0
66 Ontario Toronto East Toronto Illinois Chicago West Side 0.0
95 Ontario Toronto East York Illinois Chicago West Side 0.0
153 Ontario Toronto Mississauga Illinois Chicago West Side 0.0
211 Ontario Toronto Queen's Park Illinois Chicago West Side 0.0
240 Ontario Toronto Scarborough Illinois Chicago West Side 0.0
269 Ontario Toronto West Toronto Illinois Chicago West Side 0.0
298 Ontario Toronto York Illinois Chicago West Side 0.0
9 Ontario Toronto Central Toronto New York New York Bronx 0.0
38 Ontario Toronto Downtown Toronto New York New York Bronx 0.0
67 Ontario Toronto East Toronto New York New York Bronx 0.0
96 Ontario Toronto East York New York New York Bronx 0.0
154 Ontario Toronto Mississauga New York New York Bronx 0.0
212 Ontario Toronto Queen's Park New York New York Bronx 0.0
241 Ontario Toronto Scarborough New York New York Bronx 0.0
270 Ontario Toronto West Toronto New York New York Bronx 0.0
299 Ontario Toronto York New York New York Bronx 0.0
10 Ontario Toronto Central Toronto New York New York Brooklyn 0.0
39 Ontario Toronto Downtown Toronto New York New York Brooklyn 0.0
68 Ontario Toronto East Toronto New York New York Brooklyn 0.0
97 Ontario Toronto East York New York New York Brooklyn 0.0
155 Ontario Toronto Mississauga New York New York Brooklyn 0.0
213 Ontario Toronto Queen's Park New York New York Brooklyn 0.0
242 Ontario Toronto Scarborough New York New York Brooklyn 0.0
271 Ontario Toronto West Toronto New York New York Brooklyn 0.0
300 Ontario Toronto York New York New York Brooklyn 0.0
11 Ontario Toronto Central Toronto New York New York Manhattan 0.0
40 Ontario Toronto Downtown Toronto New York New York Manhattan 0.0
69 Ontario Toronto East Toronto New York New York Manhattan 0.0
98 Ontario Toronto East York New York New York Manhattan 0.0
156 Ontario Toronto Mississauga New York New York Manhattan 0.0
214 Ontario Toronto Queen's Park New York New York Manhattan 0.0
243 Ontario Toronto Scarborough New York New York Manhattan 0.0
272 Ontario Toronto West Toronto New York New York Manhattan 0.0
301 Ontario Toronto York New York New York Manhattan 0.0
12 Ontario Toronto Central Toronto New York New York Queens 0.0
41 Ontario Toronto Downtown Toronto New York New York Queens 0.0
70 Ontario Toronto East Toronto New York New York Queens 0.0
99 Ontario Toronto East York New York New York Queens 0.0
157 Ontario Toronto Mississauga New York New York Queens 0.0
215 Ontario Toronto Queen's Park New York New York Queens 0.0
244 Ontario Toronto Scarborough New York New York Queens 0.0
273 Ontario Toronto West Toronto New York New York Queens 0.0
302 Ontario Toronto York New York New York Queens 0.0
14 Ontario Toronto Central Toronto Texas Dallas Downtown Dallas 0.0
43 Ontario Toronto Downtown Toronto Texas Dallas Downtown Dallas 0.0
72 Ontario Toronto East Toronto Texas Dallas Downtown Dallas 0.0
101 Ontario Toronto East York Texas Dallas Downtown Dallas 0.0
159 Ontario Toronto Mississauga Texas Dallas Downtown Dallas 0.0
217 Ontario Toronto Queen's Park Texas Dallas Downtown Dallas 0.0
246 Ontario Toronto Scarborough Texas Dallas Downtown Dallas 0.0
275 Ontario Toronto West Toronto Texas Dallas Downtown Dallas 0.0
304 Ontario Toronto York Texas Dallas Downtown Dallas 0.0
16 Ontario Toronto Central Toronto Texas Dallas Fair Park 0.0
45 Ontario Toronto Downtown Toronto Texas Dallas Fair Park 0.0
74 Ontario Toronto East Toronto Texas Dallas Fair Park 0.0
103 Ontario Toronto East York Texas Dallas Fair Park 0.0
161 Ontario Toronto Mississauga Texas Dallas Fair Park 0.0
219 Ontario Toronto Queen's Park Texas Dallas Fair Park 0.0
248 Ontario Toronto Scarborough Texas Dallas Fair Park 0.0
277 Ontario Toronto West Toronto Texas Dallas Fair Park 0.0
306 Ontario Toronto York Texas Dallas Fair Park 0.0
18 Ontario Toronto Central Toronto Texas Dallas Far South Dallas 0.0
47 Ontario Toronto Downtown Toronto Texas Dallas Far South Dallas 0.0
76 Ontario Toronto East Toronto Texas Dallas Far South Dallas 0.0
105 Ontario Toronto East York Texas Dallas Far South Dallas 0.0
163 Ontario Toronto Mississauga Texas Dallas Far South Dallas 0.0
221 Ontario Toronto Queen's Park Texas Dallas Far South Dallas 0.0
250 Ontario Toronto Scarborough Texas Dallas Far South Dallas 0.0
279 Ontario Toronto West Toronto Texas Dallas Far South Dallas 0.0
308 Ontario Toronto York Texas Dallas Far South Dallas 0.0
21 Ontario Toronto Central Toronto Texas Dallas Northwest Dallas 0.0
50 Ontario Toronto Downtown Toronto Texas Dallas Northwest Dallas 0.0
79 Ontario Toronto East Toronto Texas Dallas Northwest Dallas 0.0
108 Ontario Toronto East York Texas Dallas Northwest Dallas 0.0
166 Ontario Toronto Mississauga Texas Dallas Northwest Dallas 0.0
224 Ontario Toronto Queen's Park Texas Dallas Northwest Dallas 0.0
253 Ontario Toronto Scarborough Texas Dallas Northwest Dallas 0.0
282 Ontario Toronto West Toronto Texas Dallas Northwest Dallas 0.0
311 Ontario Toronto York Texas Dallas Northwest Dallas 0.0
22 Ontario Toronto Central Toronto Texas Dallas Oak Cliff Area 0.0
51 Ontario Toronto Downtown Toronto Texas Dallas Oak Cliff Area 0.0
80 Ontario Toronto East Toronto Texas Dallas Oak Cliff Area 0.0
109 Ontario Toronto East York Texas Dallas Oak Cliff Area 0.0
167 Ontario Toronto Mississauga Texas Dallas Oak Cliff Area 0.0
225 Ontario Toronto Queen's Park Texas Dallas Oak Cliff Area 0.0
254 Ontario Toronto Scarborough Texas Dallas Oak Cliff Area 0.0
283 Ontario Toronto West Toronto Texas Dallas Oak Cliff Area 0.0
312 Ontario Toronto York Texas Dallas Oak Cliff Area 0.0
23 Ontario Toronto Central Toronto Texas Dallas Oak Lawn 0.0
52 Ontario Toronto Downtown Toronto Texas Dallas Oak Lawn 0.0
81 Ontario Toronto East Toronto Texas Dallas Oak Lawn 0.0
110 Ontario Toronto East York Texas Dallas Oak Lawn 0.0
168 Ontario Toronto Mississauga Texas Dallas Oak Lawn 0.0
226 Ontario Toronto Queen's Park Texas Dallas Oak Lawn 0.0
255 Ontario Toronto Scarborough Texas Dallas Oak Lawn 0.0
284 Ontario Toronto West Toronto Texas Dallas Oak Lawn 0.0
313 Ontario Toronto York Texas Dallas Oak Lawn 0.0
26 Ontario Toronto Central Toronto Texas Dallas South Central Dallas 0.0
55 Ontario Toronto Downtown Toronto Texas Dallas South Central Dallas 0.0
84 Ontario Toronto East Toronto Texas Dallas South Central Dallas 0.0
113 Ontario Toronto East York Texas Dallas South Central Dallas 0.0
171 Ontario Toronto Mississauga Texas Dallas South Central Dallas 0.0
229 Ontario Toronto Queen's Park Texas Dallas South Central Dallas 0.0
258 Ontario Toronto Scarborough Texas Dallas South Central Dallas 0.0
287 Ontario Toronto West Toronto Texas Dallas South Central Dallas 0.0
316 Ontario Toronto York Texas Dallas South Central Dallas 0.0

Neighborhoods level

Let us compare similarity of city's neighborhoods.

Toronto, Ontario
New York, New York
Chicago, Illinois
Dallas, Texas

Discussion

Our analysis show that the most similar city to Toronto, where the employee lives, is Chicago. We will advice to the employee to choose that city to relocate.

Of course, our analysis does not include the employee's preferences and habits. Some venues may not be attractive to the employee and by removing them we may get different results.

Note, that Forsquare provides living data, which is updated together with analyzed cities and peoples preferences, so the results are just snapshot taken at the moment. Anyway it will be interesting how the cities will look like in the future.

Conclusion

Purpose of this project was to give the employee imagination about three different cities to leave, one of them musts be chosen by the employee for future place to leave.

Final decision, which the employee musts take will base on similarity of cities, which will be provided to the employee by the employer. Additionally, the employee will receive lists of similar boroughs and neighborhoods, what helps him to orientate in the chosen city.